Supervised approaches for protein function prediction by topological data analysis
Topological Data Analysis is a novel approach, useful whenever data can be described by topological structures such as graphs. The aim of this paper is to investigate whether such tool can be used in order to define a set of descriptors useful for pattern recognition and machine learning tasks. Specifically, we consider a supervised learning problem with the final goal of predicting proteins' physiological function starting from their respective residue contact network.